IRJET- A Secured Method of Data Aggregation for Wireless Sensor Networks in the Presence of Collusion Attacks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 259
A Secured method of Data Aggregation for Wireless Sensor Networks in
the Presence of Collusion Attacks
Mohith Aiyappa S1, Ravi Kumar M N2
1 PG Scholar, Dept. of E&C, Malnad college of engineering, Karnataka, India
2 Associate Professor, Dept. of E&C, Malnad college of engineering, Karnataka, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In today’s world Wireless Sensor Networks
(WSNs) plays a major role in various applications ranging
from weather monitoring to military and surveillance
applications. In each of these applications the sensor nodes
senses the data and transmits the sensed data to the sink
node. In earlier days data is transmitted to the sink directly
without preprocessing using either single hop or by multi
hop techniques in the network, hence this method consumes
more energy. Data aggregation is an effective technique in
WSN because it reduces the number of packets to be sent to
sink and increases the lifetime of sensor network by
aggregating the similar packets. Secure data transmission is
a crucial need for achieving the QOS in WSN. Secure data
aggregation technique is one of the very efficient technique
to overcome the problem of weaker node attacks. To achieve
this, IF Algorithm is used which is most effective solution. IF
Algorithm is an efficient and reliable option for WSN
because they solve both problems of data aggregation and
data trustworthiness estimation using a single iterative
procedure. In this paper planned to concentrate on many IF
Algorithm which make robust against collusion attacks than
simply averaging method. In WSN aggregation plays an
important role in improving capacity. Data aggregation
could be of two types, Exact and Approximate. Collusion
attack means attacker tries to corrupt transmitting data,
hence there will be a mismatch in aggregation.it is the
agreement between nodes to act together secretly or
illegally in order to deceive or cheat original data.
To avoid collusion IF algorithm has been
implemented. Iteration is the act of repeating a process,
either to generate a unbounded sequence of outcomes or
with the aim of approaching a desired goal, target or result.
Each repetition of the process is also called as an Iteration.
In mathematical Iteration function is used by applying a
function repeatedly using the output from one iteration as
the input to the next. Iterative filtering algorithms are a tool
for maximum likelihood inference on partially observed
dynamical systems. In this project different IF algorithm has
been used to avoid collusion and get a proper result.
Key Words: Wireless Sensor Networks, Sensor
Nodes, IF Algorithm, Collusion Attacks, Secure
Data Aggregation.
1. INTRODUCTION
The primary use of Wireless Sensor Networks(WSN) is to
collect and process data. WSN are emerging as the widely
used technology in the present day world. A wireless
sensor networks consists of a collection of these nodes
that have the facility to sense, process data and
communicate with each other via wireless connection.
WSN is a network system comprised of spatially
distributed devices using the wireless sensor nodes to
monitor physical or environmental situations such as
sound, temperature and motion. The main goal of data
aggregation is to gather and aggregate data in an energy
efficient manner so that network lifetime is enhanced. Fig
1 shows the network model for WSN. The sensor nodes
are divided into different clusters and each cluster
appointed as a cluster head. data are periodically
monitoring from each node and then pass their data to the
cluster head. finally cluster head processing that data and
pass to the base station. WSN offer an increasingly
attractive method of data gathering in distributed system
architectures and dynamic access via wireless
connectivity. The process of grouping sensor nodes in a
densely deployed large scale sensor network is known as
clustering. sensor networks are the collection of sensor
nodes which cooperatively send sensed data to base
station. As sensor nodes are battery driven an efficient
utilization of power is essential in ore use networks for
long duration.
In WSN aggregation plays an important role in improving
capacity. Data aggregation could be of two types. Exact and
Approximate. collusion attack means attacker tries to
corrupt transmitting data, hence there will be a mismatch
in aggregation.it is the agreement between nodes to act
together secretly or illegally in order to deceive or cheat
original data.
To avoid collusion IF algorithm has been implemented.
Iteration is the act of repeating a process, either to
generate a unbounded sequence of outcomes or with the
aim of approaching a desired goal, target or result. Each
repetition of the process is also called as an Iteration. In
mathematical Iteration function is used by applying a
function repeatedly using the output from one iteration as
the input to the next. Iterative filtering algorithms are a
tool for maximum likelihood inference on partially
observed dynamical systems. In this project different IF

algorithm has been used to avoid collusion and get a
proper result.
Fig. 1.Model for WSN
2. RELATED WORK
Many actions are discussed about different algorithm
related to secure data aggregation. Aggregation problem
has been solved by, investigate the relationship between
security and data aggregation process in WSN. A taxonomy
of secure data aggregation protocols is given by looking
over the current “state-of-the-art” work in this area [1]
Synopsis diffusion approach make the protect against the
attack launched by the compromised nodes. The algorithm
is to enable the base station to securely process predicate
count or sum even in the presence of such an attack.[2]
The computational proficient strategy to process a
weighted normal (which we will call strong average)of
sensor measurements, which appropriately takes sensor
deficiencies and sensor noise into consideration[3]
The security vulnerabilities of data aggregation system
and present a survey of robust and secure aggregation
protocols that are resilient to false data injection
attacks.[4]
The algorithm present an analysis framework that allows
for general decomposition of existing reputation systems.
the attacks are classified against reputation systems by
identifying which system components and design choices
are the target of attacks.[5]
The aggregation strategies become bandwidth intensive
when combined with fault tolerant, multipath routing
methods often used in these environments.[6]
To address the security issue one technique has been
proposed that is to provide security to network from an
attacker and also enhance the robustness and accuracy of
an information. Iterative filtering algorithm is one of the
algorithm to perform secure data aggregation and also
provides security to the entire network.[7]
3. METHODOLOGY
In our proposed system we have considered IF algorithms
has been proposed.
Fig. 2.Methodology
The above figure shows the block diagram and flow
diagram of the iterative filter algorithm.
4.ITERATIVE FILTERING ALGORITHM
Iteration is the demonstration of repeating process either
to produce an unbounded grouping of result or with the
aim of approaching a desired goal, target or result. Each
process of repetition is likewise called an iteration and the
result of one iteration are utilized as the beginning stage
for the next iteration.

Iteration in arithmetic may refer to the way toward
emphasizing a capacity i.e. applying a function repeatedly,
utilizing the output from one cycle as the input to the next.
cycle apparently simple functions can create complex
behaviours and difficult issues.
In computational arithmetic, an iterative technique is a
numerical methodology that produces arrangement of
enhancing approximate solutions for a class of problems
4.1 Benefits of IF Algorithm
• Some Iterative algorithms are designed for
accuracy.
• Round off error are subjected to direct method.
• Iterating function reduces error to zero.
• It produces answer in a faster manner.
• It solves many matrix functions.
• speeds depends on number of non zero elements
and total number of elements.
• It starts with approximate answers that is output
can be acted as next input.
• Each iteration improves accuracy.
• During iteration matrix can not be changed so it
can be operated efficiently.
4.2 Steps of IF Algorithm
• In the first step initialize the first round of
iteration. taking the first round of output as the
input to the next round
• In the second step initialize inputs as X,n,m where
X is the block of readings and n is the number of
sensors and m is the number of readings for each
sensors.
• In the third step compute the output of reputation
vector r that is output can be represented as r.
• In the next step initialize weight by giving equal
credibility that is w(0) = 1 and multiplying that
weight with X.
• After multiplying compute reputation vector r(l+1),
assume l=0.
• In the sixth step compute the corresponding
weight that is w(l+1).
• In the final step repeat the above steps until the
reputation vector has converged that means if the
aggregate values are same for some iterations then
stop the process.
Fig. 3.Flowchart of IF Algorithm
4.3 Modules for secure data aggregation using IF
1) Node creation with weight factors assigned to
source.
2) Data aggregation in multiple sources.
3) Find bias and unbiased readings using IF.
4) Secure data aggregation using IF.
5. MATHEMATICAL ANALYSIS
In the reputation system of iterative filtering we will see
the context of data aggregation in WSN and the weakness
of the algorithm for a possible collusion attack.
Consider a wireless sensor network with n number of
sensors denoted by where . Data
aggregator can be operated only one block of readings at a
time.each block can be operated at a readings of m
consecutive instants.
The matrix can be represented as
where The
output vector is for a time instants
The reputation vector can be computed iteratively and
simultaneously with a sequence of weights
. Initially the iterating procedure starts with
giving equal number of weights to all sensors that is
. Then the reputation vector can be calculated
as

∑
The begining of the reputation vector can be represented
as that is .
is just the simple average of readings of all sensors at
each particular instants.The new weight vector can
be computed as a function of then
‖ ‖
Function is called as the discriminant function
• Reciprocal
• Exponential
• Affine
The above formulas are based on the computation of IF
Algorithm.
6. COLLUSION ATTACK SCENARIO
Collusion attack means a secret understanding between
two or more persons to gain something illegally.In wireless
sensor network collusion attack means some hackers inject
false data to the original node to mismatch the aggregate
readings.In this scenario attacker is able to mislead the
reported data values.
Let us consider three scenarios as shown in the below
figure. Figure 3.2 shows the different scenarios against IF
algorithm.
Fig. 4. Attacks scenario in IF
In the first scenario all sensors are reliable and the result
of the IF calculations is near to the actual value.
In the second scenario an adversary compromises two
sensor hubs, and modifies the readings of these values
such that the normal average of all sensor readings is
skewed towards a lower esteem. In that case IF algorithm
punishes them and allots to them lower weights, because
their values are a long way from the estimation of other
sensors. Iterative Filter algorithm is strong against false
data information because the compromised nodes
individually falsify the readings without any learning
about the aggregation calculation.
In the Third scenario an adversary employs three
compromised nodes in order to launch a collusion attack.
It listens to the reports of sensors in the network and
instructs the two compromised sensor nodes to report
values far from the true value of the measured quantity.
7. RESULT ANALYSIS
Fig. 5. Unbiased error using MLE
Fig. 6.Bias error
Fig. 7.Unbiased error

Fig. 8. Correlated noise
Fig.9. dVKD- Reciprocal
Fig. 10. dKVD- Affine noise
Fig.11. Zhou
Fig.12. Laureti
Fig.13. Robust aggregate Reciprocal
Fig.14. Robust aggregate Affine
The above figure shows the result of different IF
algorithms. Different IF algorithms give different types of
data aggregate readings.
8. COMPARSION RESULT
Fig.15. Network Lifetime
Fig.16. Energy Consumption
Fig.17. Throughput

9. CONCLUSION
In wireless sensor networks, an attacker can inject false
data to damage the network data integrity by utilizing a
compromised node Existing researches did not combine
data aggregation, data Confidentiality, data integrity and
false data detection together well. In this paper we
proposed an improvement for the IF algorithms by
providing an initial approximation of the trustworthiness
of sensor nodes which makes the algorithms not only
collusion robust, but also more accurate and faster
converging.so finally we concluded that proposed IF
algorithm is better than existing algorithm like network
lifetime, energy consumption and throughput. In future,
we will investigate whether our approach can protect
against compromised aggregators.
REFERENCES
[1] Y. X. Suat Ozdemir Secure data aggregation in wireless
sensor networks: A comprehensive overview, Elsevier
computer Networks 2009
[2] Z. H. L. Sankardas Roy, Mauro Conti and S. Jajodia
Secure data aggregation in wireless sensor networks:
Filtering out the attackers impact,IEEE Transactionon
Information forensic and security., vol. 09, no. 4, 2014.
[3] W. H. Chun Tung Chou Efficient computation of robust
average of compressive sensing data in wireless
sensor networks in the presence of sensor faults ,IEEE
journal 2010
[4] Sanjeev SETIA , Sankardas ROY and Sushil JAJODIA
Secure Data Aggregation in Wireless Sensor
Networks” Department of Computer Science
Proceedings of IEEE International Conference 2010
[5] Kevin Hoffman, David Zage, Cristina Nita-Rotaru A
Survey of Attack and Defense Techniques for
Reputation Systems” Department of Computer Science
and CERIAS, Purdue University, 2007
[6] George Kollios, John Byers, Jeffrey Considine, Marios
Hadjieleftheriou, and Feifei Li Robust Aggregation in
Sensor Networks” Department of Computer Science,
Boston University, 2005 IEEE
[7] X.Wu,Y.Xiong,W.Huang,H.Shen,M.Li. An efficient
compressive data gathering routing scheme for large-
scale wireless sensor networks,
ElsevierComput.Electr.Eng,39(2013).1935-1946.
[8] K.Suriya, R.Dhanagopal An Efficient Apporach For
Secure Data Aggregation Technique For Wsn In The
Presence Of Security Attacks,Department of ECE, 2015
IJARTET
[9] C.de Kerchove and P. Van Dooren Iterative filtering in
reputation systems, , SIAM J. Matrix Anal. Appl., vol. 31, no.
4, pp. 1812-1834, Mar. 2010.

IRJET- A Secured Method of Data Aggregation for Wireless Sensor Networks in the Presence of Collusion Attacks

Recommended

More Related Content

What's hot (17)

Similar to IRJET- A Secured Method of Data Aggregation for Wireless Sensor Networks in the Presence of Collusion Attacks (20)

More from IRJET Journal (20)

Recently uploaded (20)

IRJET- A Secured Method of Data Aggregation for Wireless Sensor Networks in the Presence of Collusion Attacks